An unbiased approach of molecular characterization of the endometrium: toward defining endometrial-based infertility

Abstract Infertility is a complex condition affecting millions of couples worldwide. The current definition of infertility, based on clinical criteria, fails to account for the molecular and cellular changes that may occur during the development of infertility. Recent advancements in sequencing technology and single-cell analysis offer new opportunities to gain a deeper understanding of these changes. The endometrium has a potential role in infertility and has been extensively studied to identify gene expression profiles associated with (impaired) endometrial receptivity. However, limited overlap among studies hampers the identification of relevant downstream pathways that could play a role in the development of endometrial-related infertility. To address these challenges, we propose sequencing the endometrial transcriptome of healthy and infertile women at the single-cell level to consistently identify molecular signatures. Establishing consensus on physiological patterns in endometrial samples can aid in identifying deviations in infertile patients. A similar strategy has been used with great success in cancer research. However, large collaborative initiatives, international uniform protocols of sample collection and processing are crucial to ensure reliability and reproducibility. Overall, the proposed approach holds promise for an objective and accurate classification of endometrial-based infertility and has the potential to improve diagnosis and treatment outcomes.


Introduction
Infertility is a complex and multifactorial condition that affects millions of couples worldwide (Boivin et al., 2007;Mascarenhas et al., 2012).To date, despite advances in ARTs, success rates of fertility treatments have not substantially improved (Andersen et al., 2005;Smith et al., 2015;Kushnir et al., 2017) and the underlying causes of infertility remain unknown in up to 30% of the infertile couples (Gunn and Bates, 2016).The classification of infertility is currently based on clinical, subjective criteria that are the resultant of consensus (Zegers-Hochschild et al., 2017).However, this classification is insufficient because it does not take into account the potential molecular and cellular changes that may occur during a couple's process of becoming infertile.Thanks to advances in sequencing technology and single-cell analysis (Li and Wang, 2021) we can now have a deeper understanding of the molecular and cellular changes in infertility.
While these studies have revealed hundreds of significantly upand downregulated genes in receptive and non-receptive endometrium, there is strikingly little overlap of markers between studies, as demonstrated in Table 1.This complicates the study of relevant downstream pathways that help us understand the role of the endometrium in reproductive failure or success.The heterogeneity between studies is ascribed to differences in study methodologies, such as timing and method of endometrial sampling, selection criteria of the study population, transcriptome profiling methods, genome annotation versions used, pipelines for data processing, and the absence of consistent standards for data presentation (Altm€ ae et al., 2017) (Tables 1 and 2).Currently, the use of endometrial receptivity markers in clinical practice is still controversial, as the evidence for improving clinical outcome is insufficient (Liu et al., 2022).A crucial problem arises from linking often very detailed sequencing information to definitions of fertility and infertility that, as mentioned, are formulated too generally.
We argue, that by sequencing the endometrial transcriptome of healthy and infertile women at single-cell level, it should be possible to consistently identify molecular and cellular signatures that can be used to classify endometrial-based infertility.We propose to establish endometrial gene expression profiles to first reach consensus on physiological patterns as determined in endometrial samples.Then, these new patterns can be used to study infertile patients whether and where they deviate from these basic patterns.Such an approach can contribute to a much greater understanding of the endometrial processes potentially underlying infertility.It may also lead to a more objective and accurate classification of infertility, which can ultimately greatly individualize and improve diagnosis and treatment.This approach also offers a much better possibility to compare results   from different research groups in detail.Though the focus here is on transcriptomics analysis, the approach can be adapted for any type of molecular analysis.

Definition of fertile and infertile
Although there are varying perspectives on defining healthy, fertile women, we propose identification based on standard anatomical and endocrinologic criteria: a normal uterus in medical imaging, regular menstrual cycles and a history of a previous pregnancy established within a year resulting in a full-term delivery.Regarding the selection criteria for the group of infertile women, we recommend the inclusion of women with unexplained infertility and recurrent implantation failure in order to increase the chance of identifying an endometrial factor.While the application of these criteria may still encompass the inclusion of women with a variety of genetic profiles, we expect that our proposed method will uncover distinct patterns of endometrial function.Moving forward, groups are categorized based on molecular patterns, extending the analysis beyond clinical phenotypes to enable deeper exploration of underlying biological mechanisms.

Sample collection
To ensure the reliability and reproducibility of such an approach, an international standardization on how to collect and process samples is essential.This would involve developing a standardized protocol for the collection, storage, and processing of endometrial tissue and single cells to ensure consistency across different studies and laboratories.While logistical constraints may limit full standardization, this does not necessarily impede analysis.For example, when considering biopsy planning, natural cycle biopsies offer a physiologically accurate endometrial representation, while tissue obtained during artificial cycles allows rapid biobanking, despite potential gene expression alterations from exogenous hormones.As our approach involves clustering biopsies by gene profiles, the focus lies on exploring underlying biological mechanisms.Thus, regardless of the biopsy source, those with similar gene profiles will group together.This facilitates the identification of common pathways and the formulation of hypotheses that may play a role in endometrialbased infertility.Finally, linking clinical data to these biopsies can help in shaping these hypotheses.

A large single-cell transcriptome database
While several databases of endometrial single-cell transcriptomes already exist (Simon, 2020;Wang et al., 2020;Garcia-Alonso et al., 2021), sample sizes are relatively small.Large, geographically broad collaborative initiatives should be encouraged, allowing rapid and large-scale biobanking of endometrial tissue within a consortium with uniform protocols and standardized methods, more clinical, molecular and bioinformatical expertise and better generalizability of the data, altogether improving the quality of the research.Single-cell sequencing allows for the dissection of gene profiles in individual cells, enabling the analysis of patterns for each cell type in each cycle stage and accounting for the substantial variation in gene expression within and between menstrual cycles.Incorporation of single-cell high-throughput data from in vitro models that recapitulate in vivo conditions, such as the organoid models, could provide valuable data by disease modeling.In addition, endometrial cells that are obtained in a noninvasive way, such as via menstrual blood, are an alternative to the more invasive endometrial biopsy (Shih et al., 2022), and could facilitate largescale banking of endometrial tissue and data.This would simplify the inclusion of tissue from healthy donors not receiving clinical care, albeit limited to the menstrual phase.Additional sources could involve women undergoing preimplantation genetic testing cycles or donor insemination, predominantly comprising women with a male factor infertility, single women and lesbian couples, in whom an endometrial factor is likely absent.
A similar strategy has been used with great success in cancer research.The TCGA (Cancer Genome Atlas) project, started in 2005, generated comprehensive molecular profiles of different types of cancer (The Cancer Genome Atlas, 2005;Tomczak et al., 2015).Molecular profiling has transformed cancer treatment by guiding the selection of targeted therapies, identifying biomarkers, understanding resistance mechanisms, guiding the use of immunotherapies and facilitating clinical trials (Lee et al., 2018;Liu et al., 2023;Sahm et al., 2023;Saito-Adachi et al., 2023).This personalized approach has improved treatment outcomes and reduced unnecessary side effects.Combined with recently highly developed analytics technology including AI-based methods, this approach has opened doors for the development of innovative therapies in the era of precision medicine.A similar approach could be applied to endometrial-based infertility, where a large database of sequencing data from healthy and infertile endometrial tissue at single-cell level could provide new insights into the molecular mechanisms potentially underlying endometrial-based infertility, which may lead to new therapeutic strategies.However, to confirm the practical effectiveness of this approach, the findings of the hypothesis-free work still need to be extrapolated to the clinic.To this end, a future study will need to link the molecular data with clinical data on (in)fertility.

Challenges in single-cell transcriptomics
Finally, although single-cell transcriptomics studies offer valuable insights into gene expression at a cellular level, they come with certain limitations.Technical noise, data complexity and dropout events can compromise accuracy and analysis.Additionally, defining cell types accurately, addressing batch effects and managing scalability present additional challenges (Hicks et al., 2018;L€ ahnemann et al., 2020;Adil et al., 2021).Nonetheless, ongoing advancements in technology and computational methods address many of these disadvantages.

Conclusions
In conclusion, a classification system derived from sequencing data of endometrium tissue of healthy and infertile women at single-cell level can provide a more objective and accurate classification of endometrial-based infertility.However, the standardization of sample collection and processing is crucial to ensure the reliability and reproducibility of such an approach.While full standardization may present challenges, our key message underscores the importance of establishing a large, accessible biobank/ database which can aid in identifying specific molecular patterns.Such an approach has been successful in cancer research and could provide new insights into the molecular mechanisms underlying endometrial-based infertility, ultimately leading to improved diagnosis and treatment.

Table 1 .
Overview of top 10 most significantly up-and downregulated genes among endometrial transcriptome studies comparing reproductive failure and success groups.

Table 2 .
Overview of study methodologies and number of differentially expressed genes among endometrial transcriptome studies comparing reproductive failure and success groups.